Optimal Resource Allocation and Task Segmentation in IoT Enabled Mobile Edge Cloud

Recent development toward innovative applications and technologies like self-driving, augmented reality, smart cities, and various other applications leads to excessive growth in the number of devices. These devices have finite computation resources and cannot handle the applications that require extensive computation with minimal delay. To overcome this, the mobile edge cloud (MEC) emerges as a practical solution that allows devices to offload their extensive computation to MEC located in their vicinity; this will lead to succeeding the arduous delay of the millisecond scale: requirement of 5th generation communication system. This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name Estimation of Optimal Resource Allocator (EORA) is designed to optimize the function by keeping track of statistics of each candidate of the population. Using EORA, a comparative analysis of the hybrid approach (partial offloading) and edge computation only is performed. Results reveal the fundamental trade-off between both of these models. Simultaneously, the impact of devices’ computational capability, data volume, and computational cycles requirement on task segmentation is analyzed. Simulation results demonstrate that the hybrid approach: partial offloading scheme reduces the task’s computation time and outperforms edge computing only.